AI agents are moving quickly from controlled pilots into day-to-day enterprise workflows. They access data, trigger actions, and make decisions with limited human oversight. Security teams are already seeing the downside. Tools designed for users and applications struggle to explain whether an AI agent is behaving normally or drifting into risky territory.
This is the gap
Exabeam is addressing with its connected system for AI agent behavior analytics and AI security posture insight. The focus is not just on governance, but on understanding what AI agents are actually doing inside live environments.
Moving beyond AI governance
Many vendors frame AI security around policies, access controls, and compliance. That helps with oversight, but it does not tell a SOC whether an AI agent is actively creating risk. As
Craig Patterson, Global Channel Chief at Exabeam, explains to MSSP Alert, “most vendors focus on AI governance with policies, compliance, and access controls, but that doesn't detect actual threats.”
He says the shift Exabeam is making is toward behavior. “For the SOC teams our partners manage, this means real visibility into how AI agents behave within their customers' environments. They can identify anomalous patterns in how agents interact with data, systems, and users.” When agents move outside their established baseline, teams can spot and investigate that change quickly.
Applying UEBA to AI agents
At the core of the platform is the extension of user and entity behavior analytics to AI agents. Instead of treating agent activity as generic telemetry, Exabeam builds behavioral baselines and highlights deviations that matter.
Patterson describes it as applying “our proven UEBA methodology, refined over a decade, directly to AI agents.” The result, he says, is that partners’ security teams get “the behavioral insight they need to identify risk early and investigate AI agent activity quickly, not just manage policies around it.” For SOCs, the practical benefit is less guesswork and faster investigations when something looks off.
What this looks like in multi-tenant MSSP environments
For MSSPs, the problem is harder. Each customer’s AI agents behave differently, and generic thresholds create noise. Exabeam’s approach is to establish independent baselines per tenant while still learning from activity across the broader customer base.
“We've built the platform to establish independent behavioral baselines per tenant while leveraging cross-tenant intelligence to improve detection accuracy,” Patterson says. Alerts are driven by what is normal for each customer, not by shared assumptions. That helps reduce false positives without weakening detection.
There is also a portfolio-level advantage. “The real value for MSSPs is that they can apply threat patterns identified at one customer to proactively hunt for similar activity across their portfolio,” he explains. That allows providers to respond faster to new AI agent abuse techniques while maintaining customer isolation.
Reducing analyst workload, not adding to it
AI agents often perform tasks that look legitimate because they automate human workflows. Traditional tools miss this subtle abuse, which increases alert volume without improving signal. Patterson argues that behavioral analytics changes that balance.
“We reduce workload by prioritizing investigations based on behavioral deviation rather than overwhelming analysts with every AI agent action,” he says. Instead of reviewing endless events, analysts are directed to outliers such as agents accessing unusual resources or operating outside normal hours. When alerts fire, “analysts receive behavioral context about what changed, which accelerates investigation time.”
For MSSPs, that efficiency matters. Patterson notes this approach allows providers to support more customers “without proportionally increasing headcount, which directly impacts their profitability and scalability.”
How MSSPs are packaging AI agent security
AI agent security is also forcing service providers to rethink how they package offerings. Patterson is clear that this is not a niche add-on. “When AI agents are already deployed at scale, AI agent behavior analytics should be integrated into core MDR services,” he says, warning that excluding agent monitoring creates blind spots.
At the same time, not all customers are at the same maturity level. “We're seeing MSSPs adopt a hybrid approach with foundational AI agent monitoring in core MDR and premium tiers for deeper analytics and strategic guidance,” Patterson explains. The key, in his view, is scale: “treating AI agent security as an evolution of existing TDIR workflows, not as a separate service line.”
As AI agents become embedded in enterprise operations, security teams need more than policies and dashboards. They need a way to understand agent behavior, investigate issues quickly, and measure whether controls are improving over time. Exabeam’s connected system reflects a broader shift toward treating AI agents as first-class security entities. For SOCs and MSSPs, the so-what is clear: visibility into behavior is becoming essential as agent adoption accelerates.
